Scatter Plot Matrix Last Updated : 23 May, 2024 Summarize Comments Improve Suggest changes Share Like Article Like Report In a dataset, for k set of variables/columns (X1, X2, ....Xk), the scatter plot matrix plot all the pairwise scatter between different variables in the form of a matrix. Scatter plot matrix answer the following questions: Are there any pair-wise relationships between different variables? And if there are relationships, what is the nature of these relationships?Are there any outliers in the dataset?Is there any clustering by groups present in the dataset on the basis of a particular variable? For k variables in the dataset, the scatter plot matrix contains k rows and k columns. Each row and column represents as a single scatter plot. Each individual plot (i, j) can be defined as: Vertical Axis: Variable XjHorizontal Axis: Variable Xi Below are some important factors we consider when plotting the Scatter plot matrix: The plot lies on the diagonal is just a 45 line because we are plotting here Xi vs Xi. However, we can plot the histogram for the Xi in the diagonals or just leave it blank.Since Xi vs Xj is equivalent to Xj vs Xi with the axes reversed, we can also omit the plots below the diagonal.It can be more helpful if we overlay some line plot on the scattered points in the plots to give more understanding of the plot.The idea of the pair-wise plot can also be extended to different other plots such as quantile-quantile plots or bihistogram.ImplementationFor this implementation, we will be using the Titanic dataset. This dataset can be downloaded from Kaggle. Before plotting the scatter matrix, we will be performing some preprocessing operations on the dataframe to obtain it into the desired form. Python3 import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt % matplotlib inline # load titanic dataset titanic_dataset = pd.read_csv('tested.csv.xls') titanic_dataset.head() # Drop some unimportant columns in the dataset. titanic_dataset.drop(['Name', 'Ticket','Cabin','PassengerId'],axis=1, inplace=True) # check for different data types titanic_dataset.dtypes # print unique values of dataset titanic_dataset['Embarked'].unique() titanic_dataset['Sex'].unique() # Replace NAs with mean titanic_dataset.fillna(titanic_dataset.mean(), inplace=True) # convert some column into integer for representation in # scatter matrix titanic_dataset["Sex"] = titanic_dataset["Sex"].cat.codes titanic_dataset["Embarked"] = titanic_dataset["Embarked"].cat.codes titanic_dataset.head() # plot scatter matrix using pandas and matplotlib survive_colors = {0:'orange', 1:'blue'} pd.plotting.scatter_matrix(titanic_dataset,figsize=(20,20),grid=True, marker='o', c= titanic_dataset['Survived'].map(colors)) # plot scatter matrix using seaborn sns.set_theme(style="ticks") sns.pairplot(titanic_dataset, hue='Survived') PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 0 892 0 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q 1 893 1 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S 2 894 0 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q 3 895 0 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S 4 896 1 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN SPassengerId int64 Survived int64 Pclass int64 Sex object Age float64 SibSp int64 Parch int64 Fare float64 Embarked object dtype: objectSurvived Pclass Sex Age SibSp Parch Fare Embarked 0 0 3 1 34.5 0 0 7.8292 1 1 1 3 0 47.0 1 0 7.0000 2 2 0 2 1 62.0 0 0 9.6875 1 3 0 3 1 27.0 0 0 8.6625 2 4 1 3 0 22.0 1 1 12.2875 2Matplotlib Scatter matrixSeaborn Scatter matrixReferences:NIST handbook Comment More infoAdvertise with us Next Article Scatter Plot Matrix P pawangfg Follow Improve Article Tags : Machine Learning AI-ML-DS python Data Visualization ML-EDA ML-plots +2 More Practice Tags : Machine Learningpython Similar Reads Python Tutorial - Learn Python Programming Language Python is one of the most popular programming languages. 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